Air Pollution Index Estimation Model Based On Artificial Neural Network

dc.contributor.author	Mohammed Nasser, Al-Subaie
dc.date.accessioned	2022-08-30T08:14:15Z
dc.date.available	2022-08-30T08:14:15Z
dc.date.issued	2021-06-01
dc.description.abstract	Environmental conservation efforts are always dealing with a complex problem because it involves a large number of variables. However, choosing a correct model structure, and optimum training algorithm with minimum complexity is crucial. Therefore, a dimensional reduction method was implemented based on the multiway principal component analysis (MPCA) method. Three models were built in first part; ozone estimation model, particulate matter 10 (PM10) estimation model, and air pollution index (API) estimation model. Six inputs were used in ozone and PM10 models, which are nitrogen oxides( NOx), carbon monoxide (CO), sulphur dioxides (SO2), wind speed, air temperature, and relative humidity. After that, ozone and PM 10 were used as input to the API estimation model. The result shows that the implementation of the MPCA has insignificant improvement on the overall correlation factor due to the high nonlinearity of data.	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/15924
dc.language.iso	en	en_US
dc.publisher	Universiti Sains Malaysia	en_US
dc.title	Air Pollution Index Estimation Model Based On Artificial Neural Network	en_US
dc.type	Other	en_US

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